Software's effect upon the world hinges upon the hardware that interprets it. This tends not to be an issue, because we standardise hardware. AI is typically conceived of as a software ``mind'' running on such interchangeable hardware. This formalises mind-body dualism, in that a software ``mind'' can be run on any number of standardised bodies. While this works well for simple applications, we argue that this approach is less than ideal for the purposes of formalising artificial general intelligence (AGI) or artificial super-intelligence (ASI). The general reinforcement learning agent AIXI is pareto optimal. However, this claim regarding AIXI's performance is highly subjective, because that performance depends upon the choice of interpreter. We examine this problem and formulate an approach based upon enactive cognition and pancomputationalism to address the issue. Weakness is a measure of simplicity, a ``proxy for intelligence'' unrelated to compression. If hypotheses are evaluated in terms of weakness, rather than length, we are able to make objective claims regarding performance. Subsequently, we propose objectively optimal notions of AGI and ASI such that the former is computable and the latter anytime computable (though impractical).

翻译：软件对世界的影响取决于解释它的硬件。这通常不构成问题，因为我们对硬件进行了标准化。人工智能通常被设想为运行在这种可互换硬件上的软件“思维”。这形式化了心身二元论，即软件“思维”可以在任意数量的标准化身体上运行。虽然这种方法对简单应用效果良好，但我们认为，在形式化人工通用智能（AGI）或人工超级智能（ASI）时，这种方法并不理想。通用强化学习代理AIXI是帕累托最优的。然而，关于AIXI性能的这一主张高度主观，因为其性能取决于解释器的选择。我们审视了这一问题，并基于生成认知与泛计算主义提出了一种方法来解决该问题。弱度是衡量简单性的指标，是一种与压缩无关的“智能代理”。如果假设是根据弱度而不是长度进行评估，我们就能对性能做出客观论断。随后，我们提出了AGI和ASI的客观最优概念，其中前者是可计算的，后者是任意时刻可计算的（尽管不切实际）。